Ishita Dasgupta
- General Decision Sciences top 10%
- Decision-Making and Behavioral Economics 2
- Artificial Intelligence top 10%
- Topic Modeling 4
- Machine Learning and Data Classification 3
- Bayesian Modeling and Causal Inference 2
- Explainable Artificial Intelligence (XAI) 2
- Advanced Text Analysis Techniques 2
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- Caching and Content Delivery 3
- Peer-to-Peer Network Technologies 2
- Co-authors
- Samuel J. GershmanEric SchulzAntonia CreswellJames L. McClellandAndrew K. LampinenStephanie C. Y. ChanFelix HillKory W. Mathewson
- Journals
- SHILAP Revista de lepidopterología (1 paper)Psychological Review (1 paper)Biochemistry (1 paper)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Ishita Dasgupta
24 papers receiving 371 citations
Peers
Comparison fields: 5 of 88
- General Decision Sciences 44
- Artificial Intelligence 172
- Health Informatics 7
- Cognitive Neuroscience 70
- Developmental and Educational Psychology 38
Countries citing papers authored by Ishita Dasgupta
This map shows the geographic impact of Ishita Dasgupta's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ishita Dasgupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ishita Dasgupta more than expected).
Fields of papers citing papers by Ishita Dasgupta
This network shows the impact of papers produced by Ishita Dasgupta. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ishita Dasgupta. The network helps show where Ishita Dasgupta may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ishita Dasgupta, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 24 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 2 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 17 | |
| 9 | 2022 | 86 | |
| 10 | 2022 | 4 | |
| 11 | 2022 | 6 | |
| 12 | 2021 | 30 | |
| 13 | Learning Structure from the Ground up: Hierarchical Representation Learning by Chunking | 2021 | 2 |
| 14 | 2021 | 0 | |
| 15 | Meta-Learning of Compositional Task Distributions in Humans and Machines. | 2020 | 2 |
| 16 | 2020 | 38 | |
| 17 | 2020 | 26 | |
| 18 | 2018 | 20 | |
| 19 | Markov Transitions between Attractor States in a Recurrent Neural Network. | 2017 | 2 |
| 20 | 2017 | 57 |
About Ishita Dasgupta
Ishita Dasgupta is a scholar working on General Decision Sciences, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 378 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Caching and Content Delivery (3 papers), Machine Learning and Data Classification (3 papers), Peer-to-Peer Network Technologies (2 papers), Bayesian Modeling and Causal Inference (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Decision-Making and Behavioral Economics (2 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in General Decision Sciences (44 citations), Artificial Intelligence (172 citations) and Health Informatics (7 citations). Ishita Dasgupta has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Samuel J. Gershman, Eric Schulz, Antonia Creswell, James L. McClelland, Andrew K. Lampinen, Stephanie C. Y. Chan, Felix Hill, Kory W. Mathewson, Joshua B. Tenenbaum and Jane Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Psychological Review and Biochemistry.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.